Abstract
Introduction: This study aimed to assess the feasibility of applying natural language processing (NLP) to analyze real-world data (RWD) and resolve clinical problems in patients with secondary hyperparathyroidism and chronic kidney disease undergoing hemodialysis (SHPT/CKD-HD). The primary objective was to evaluate how well the guideline-recommended analytical goals are achieved in a Spanish cohort of SHPT/CKD-HD patients based on RWD. Methods: Unstructured data in the electronic health records (EHRs) from 8 hospitals were retrospectively analyzed using the EHRead® technology, based on NLP and machine learning. Variables extracted from EHRs included demographics, CKD-related clinical characteristics, comorbidities and complications, mineral and bone disorder parameter levels, and treatments at baseline, 6-month, and 12-month follow-up. Results: A total of 623 prevalent SHPT/CKD-HD patients were identified; of those, 282 fulfilled the inclusion criteria. They were predominantly elderly males with cardiovascular comorbidities, and the first cause of CKD was diabetic nephropathy. Diagnosis of SHPT was associated with an improvement in median values for PTH, calcium, and phosphate. However, the percentage of patients with normal PTH ranges remained stable during the study period (52.8–60.4%), while the percentage of patients with within-target range serum calcium or phosphate values showed an increasing trend (43.2–60% and 38.8–50%). At baseline, 74.1% of patients were using SHPT-related medication, including at least one vitamin D or analog (63.1%), phosphate binders (46.8%), and/or calcimimetics (9.6%). Conclusions: This study represents the first attempt to use clinical NLP to analyze SHPT/CKD-HD patients based on unstructured clinical data. This methodology is useful to address clinical problems based on RWD and identified a high rate of out-of-range mineral-bone analytical values in patients with HPT/CKD-HD and an increasing trend of out-of-range values for serum calcium and phosphate.